ArticleScientific data2020
Multicenter intracranial EEG dataset for classification of graphoelements and artifactual signals.
Article in Scientific data, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
6 citing papers in PubMed, 34 citations in OpenAlex.
- Enhancing biomedical signals through genetic algorithm optimized Exponentiated transmuted weibull denoising techniques.Scientific reports · 2026Article
- Identification and classification of pathology and artifacts for human intracranial cognitive research.NeuroImage · 2023Article
- Utilization of temporal autoencoder for semi-supervised intracranial EEG clustering and classification.Scientific reports · 2023Article
- Article
- Seizure likelihood varies with day-to-day variations in sleep duration in patients with refractory focal epilepsy: A longitudinal electroencephalography investigation.EClinicalMedicine · 2021Article
- Multicenter intracranial EEG dataset for classification of graphoelements and artifactual signals.Scientific data · 2020Article
Corrections and comments
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Authors and funding
15 authors at 5 institutions in 2 countries.
Funding
Abstract
EEG signal processing is a fundamental method for neurophysiology research and clinical neurology practice. Historically the classification of EEG into physiological, pathological, or artifacts has been performed by expert visual review of the recordings. However, the size of EEG data recordings is rapidly increasing with a trend for higher channel counts, greater sampling frequency, and longer recording duration and complete reliance on visual data review is not sustainable. In this study, we publicly share annotated intracranial EEG data clips from two institutions: Mayo Clinic, MN, USA and St. Anne's University Hospital Brno, Czech Republic. The dataset contains intracranial EEG that are labeled into three groups: physiological activity, pathological/epileptic activity, and artifactual signals. The dataset published here should support and facilitate training of generalized machine learning and digital signal processing methods for intracranial EEG and promote research reproducibility. Along with the data, we also propose a statistical method that is recommended for comparison of candidate classifier performance utilizing out-of-institution/out-of-patient testing.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.